一种改进的嵌入式特征选择算法及应用

A Novel Embedded Feature Selection Algorithm and Its Application

摘要:

针对非线性多分类问题，提出了一个改进的嵌入最小-最大值特征选择算法，并与支持向量机算法结合，提出了针对复杂的组合优化问题的启发式算法。为验证方法的有效性，在钢板缺陷识别工程数据集上进行了实验，表明所提出的方法具有较高的求解速度和预测准确度。

Abstract:

An improved embedded min-max feature selection algorithm was proposed for the nonlinear multi-label classification problem， and in combination with the support vector machine algorithm， a heuristic algorithm was proposed for the complex combinatorial optimization problem. The efficiency and accuracy of the proposed algorithm were verified after a series of experiments conducted on steel faults diagnosis dataset.